Several recent works have considered techniques for decentralizing FL aggregation schemes. The authors in [1]} introduced a decentralized FL scheme based on model segmentation with a gossip protocol for client sampling in each model aggregation round. In [2]}, a decentralized FL solution was proposed using the device-to-device (D2D) concept in serverless edge networks, with a graph-coloring based scheduling policy to characterize the data communications between two devices. Our recent works in [3]}, [4]} developed D2D-based semi-decentralized and hybrid FL frameworks, where we jointly modeled communication efficiency and statistical heterogeneity of data and obtained new convergence bounds for distributed ML.
